Pub Date : 2023-09-13DOI: 10.11113/ijic.v13n1-2.417
Mohammed Mousa Rashid, Nadia Adnan Shiltagh Al-Jamali
Software Defined Networking (SDN) is a modern network architectural model that manages network traffic using software. SDN is a networking scenario that modifies the conventional network design by combining all control features into a single place and making all choices centrally. Controllers are the "brains" of SDN architecture since they are responsible for making control decisions and routing packets at the same time. The capacity for centralized decision-making on routing improves the performance of the network. SDN's growing functionality and uses have led to the development of many controller systems. Every SDN controller idea or design must prioritize the control plane since it is the most crucial part of the SDN architecture. Studies have been done to examine, analyze, and evaluate the relative advantages of the many controllers that have been created in recent years. In this paper, finding the perfect controller based on derived needs (for example, the controller must have a "Java" or "Python" interface), a matching process compares controller features with requirements.
{"title":"A Useful and Effective Method for Selecting a Smart Controller for SDN Network Design and Implement","authors":"Mohammed Mousa Rashid, Nadia Adnan Shiltagh Al-Jamali","doi":"10.11113/ijic.v13n1-2.417","DOIUrl":"https://doi.org/10.11113/ijic.v13n1-2.417","url":null,"abstract":"Software Defined Networking (SDN) is a modern network architectural model that manages network traffic using software. SDN is a networking scenario that modifies the conventional network design by combining all control features into a single place and making all choices centrally. Controllers are the \"brains\" of SDN architecture since they are responsible for making control decisions and routing packets at the same time. The capacity for centralized decision-making on routing improves the performance of the network. SDN's growing functionality and uses have led to the development of many controller systems. Every SDN controller idea or design must prioritize the control plane since it is the most crucial part of the SDN architecture. Studies have been done to examine, analyze, and evaluate the relative advantages of the many controllers that have been created in recent years. In this paper, finding the perfect controller based on derived needs (for example, the controller must have a \"Java\" or \"Python\" interface), a matching process compares controller features with requirements.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The high-speed internet has led to the development of Internet of Things (IoT) with a fundamental Three-Layer IoT architecture. However, small amount of un-indicative data captured at the end level of IoT network makes the edge IoT devices susceptible to cyber-security attacks aimed at its transport layer. The Distributed Denial of Service (DDoS) poses significant cyber-security threat to the heterogenous IoT devices which are rendered vulnerable by ineffectiveness of conventional cybersecurity softwares. The literature reveals numerous studies that employed machine learning for the mitigation of IoT DDoS attacks but they lack in terms of an extensive investigation on optimization of machine learning classifiers. Therefore, this study first evaluates the prediction performance of machine learning classification algorithms trained on an authenticated/validated real-time IoT traffic dataset. The results reveal Logistic Regression (LR) as the most effective supervised machine learning classifier for detecting IoT DDoS attacks with a prediction accuracy of 97%. Following this, another investigation on the hybridization of LR with optimization algorithms yields Grasshopper Optimizer Algorithms (GOA) as the most effective optimizer in improving its prediction accuracy to 99%. Hence, the LR hybridized by GOA is developed as the optimal IoT DDoS Attack detection solution. Thus, the study serves to lay the foundation of a data-driven approach for the mitigation of the emerging variants of malicious IoT DDoS attacks such as zero-day attacks.
{"title":"Hybrid of Supervised Learning and Optimization Algorithm for Optimal Detection of IoT Distributed Denial of Service Attacks","authors":"T. Farid, M. Sirat","doi":"10.11113/ijic.v13n1.329","DOIUrl":"https://doi.org/10.11113/ijic.v13n1.329","url":null,"abstract":"The high-speed internet has led to the development of Internet of Things (IoT) with a fundamental Three-Layer IoT architecture. However, small amount of un-indicative data captured at the end level of IoT network makes the edge IoT devices susceptible to cyber-security attacks aimed at its transport layer. The Distributed Denial of Service (DDoS) poses significant cyber-security threat to the heterogenous IoT devices which are rendered vulnerable by ineffectiveness of conventional cybersecurity softwares. The literature reveals numerous studies that employed machine learning for the mitigation of IoT DDoS attacks but they lack in terms of an extensive investigation on optimization of machine learning classifiers. Therefore, this study first evaluates the prediction performance of machine learning classification algorithms trained on an authenticated/validated real-time IoT traffic dataset. The results reveal Logistic Regression (LR) as the most effective supervised machine learning classifier for detecting IoT DDoS attacks with a prediction accuracy of 97%. Following this, another investigation on the hybridization of LR with optimization algorithms yields Grasshopper Optimizer Algorithms (GOA) as the most effective optimizer in improving its prediction accuracy to 99%. Hence, the LR hybridized by GOA is developed as the optimal IoT DDoS Attack detection solution. Thus, the study serves to lay the foundation of a data-driven approach for the mitigation of the emerging variants of malicious IoT DDoS attacks such as zero-day attacks.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"19 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78819598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yee Zi Wei, Marina Md-Arshad, Adlina Abdul Samad, Norafida Ithnin
Most IoT devices are designed and built for cheap and basic functions, therefore, the security aspects of these devices are not taken seriously. Yet, IoT devices tend to play an important role in this era, where the amount of IoT devices is predicted to exceed the number of traditional computing devices such as desktops and laptops. This causes more and more cybersecurity attacks to target IoT devices and malware attack is known to be the most common attack in IoT networks. However, most research only focuses on malware detection in traditional computing devices. The purpose of this research is to compare the performance of Random Forest and Naïve Bayes algorithm in terms of accuracy, precision, recall and F1-score in classifying the malware attack and benign traffic in IoT network traffic. Research is conducted with the Aposemat IoT-23 dataset, a labelled dataset that contains IoT malware infection traffic and IoT benign traffic. To determine the data in IoT network traffic packets that are useful for threat detection, a study is conducted and the threat data is cleaned up and prepared using RStudio and RapidMiner Studio. Random Forest and Naïve Bayes algorithm is used to train and classify the cleaned dataset. Random Forest can prevent the model from overfitting while Naïve Bayes requires less training time. Lastly, the accuracy, precision, recall and F1-score of the machine learning algorithms are compared and discussed. The research result displays the Random Forest as the best machine learning algorithm in classifying the malware attack traffic.
{"title":"Comparing Malware Attack Detection using Machine Learning Techniques in IoT Network Traffic","authors":"Yee Zi Wei, Marina Md-Arshad, Adlina Abdul Samad, Norafida Ithnin","doi":"10.11113/ijic.v13n1.384","DOIUrl":"https://doi.org/10.11113/ijic.v13n1.384","url":null,"abstract":"Most IoT devices are designed and built for cheap and basic functions, therefore, the security aspects of these devices are not taken seriously. Yet, IoT devices tend to play an important role in this era, where the amount of IoT devices is predicted to exceed the number of traditional computing devices such as desktops and laptops. This causes more and more cybersecurity attacks to target IoT devices and malware attack is known to be the most common attack in IoT networks. However, most research only focuses on malware detection in traditional computing devices. The purpose of this research is to compare the performance of Random Forest and Naïve Bayes algorithm in terms of accuracy, precision, recall and F1-score in classifying the malware attack and benign traffic in IoT network traffic. Research is conducted with the Aposemat IoT-23 dataset, a labelled dataset that contains IoT malware infection traffic and IoT benign traffic. To determine the data in IoT network traffic packets that are useful for threat detection, a study is conducted and the threat data is cleaned up and prepared using RStudio and RapidMiner Studio. Random Forest and Naïve Bayes algorithm is used to train and classify the cleaned dataset. Random Forest can prevent the model from overfitting while Naïve Bayes requires less training time. Lastly, the accuracy, precision, recall and F1-score of the machine learning algorithms are compared and discussed. The research result displays the Random Forest as the best machine learning algorithm in classifying the malware attack traffic.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89754652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Norsyahidah Zulkarnain, Muhammad Salihi Abdul Hadi, N. Mohammad, I. Shogar
This paper proposes a compartmental Susceptible-Exposed-Infected-Recovered-Death (SEIRD) model for COVID-19 cases in Malaysia. This extended model is more relevant to describe the disease transmission than the SIRD model since the exposed (E) compartment represents individuals in the disease's incubation period. The mathematical model is a system of ordinary differential equations (ODEs) with time-varying coefficients as opposed to the conventional model with constant coefficients. This time dependency treatment is necessary as the epidemiological parameters such as infection rate β, recovery rate γ, and death rate μ usually change over time. However, this feature leads to an increasing number of unknowns needed to be solved to fit the model with the actual data. Several optimization algorithms under Python’s LMfit package, such as Levenberg-Marquardt, Nelder-Mead, Trust-Region Reflective and Sequential Linear Squares Programming; are employed to estimate the related parameters, in such that the numerical solution of the ODEs will fit the data with the slightest error. Nelder-Mead outperforms the other optimization algorithm with the least error. Qualitatively, the result shows that the proportion of the quarantine rule-abiding population should be maintained up to 90% to ensure Malaysia successfully reaches disease-free or endemic equilibrium.
本文提出了针对马来西亚COVID-19病例的分区易感-暴露-感染-恢复-死亡(SEIRD)模型。这个扩展模型比SIRD模型更适合于描述疾病传播，因为暴露(E)隔室代表处于疾病潜伏期的个体。该数学模型是一个具有时变系数的常微分方程(ode)系统，而不是传统的常系数模型。这种时间依赖性治疗是必要的，因为流行病学参数如感染率β、康复率γ和死亡率μ通常随时间而变化。然而，这一特征导致需要解决越来越多的未知数，以便将模型与实际数据拟合。Python LMfit包下的几种优化算法，如Levenberg-Marquardt、Nelder-Mead、Trust-Region Reflective和Sequential Linear Squares Programming;的方法来估计相关参数，从而使ode的数值解能以最小的误差拟合数据。Nelder-Mead以最小的误差优于其他优化算法。定性地说，结果表明，遵守检疫规则的人口比例应保持高达90%，以确保马来西亚成功达到无病或地方病平衡。
{"title":"Fitting Time-varying Coefficients SEIRD Model to COVID-19 Cases in Malaysia","authors":"Norsyahidah Zulkarnain, Muhammad Salihi Abdul Hadi, N. Mohammad, I. Shogar","doi":"10.11113/ijic.v13n1.397","DOIUrl":"https://doi.org/10.11113/ijic.v13n1.397","url":null,"abstract":"This paper proposes a compartmental Susceptible-Exposed-Infected-Recovered-Death (SEIRD) model for COVID-19 cases in Malaysia. This extended model is more relevant to describe the disease transmission than the SIRD model since the exposed (E) compartment represents individuals in the disease's incubation period. The mathematical model is a system of ordinary differential equations (ODEs) with time-varying coefficients as opposed to the conventional model with constant coefficients. This time dependency treatment is necessary as the epidemiological parameters such as infection rate β, recovery rate γ, and death rate μ usually change over time. However, this feature leads to an increasing number of unknowns needed to be solved to fit the model with the actual data. Several optimization algorithms under Python’s LMfit package, such as Levenberg-Marquardt, Nelder-Mead, Trust-Region Reflective and Sequential Linear Squares Programming; are employed to estimate the related parameters, in such that the numerical solution of the ODEs will fit the data with the slightest error. Nelder-Mead outperforms the other optimization algorithm with the least error. Qualitatively, the result shows that the proportion of the quarantine rule-abiding population should be maintained up to 90% to ensure Malaysia successfully reaches disease-free or endemic equilibrium.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"8 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82223515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
X. Y. Lim, Jia Yee Lim, Weng Howe Chan, Hui Wen Nies
Next Generation Sequencing (NGS) is a modern sequencing technology that can determine the sequences of RNA and DNA faster and at lower cost. The availability of NGS data has sparked numerous efforts in bioinformatics, especially in the study of genetic variation and viral sequence detection. Viral sequence detection has been one of the important processes in studying virus-induced diseases. Common methods in detecting viral sequences involve alignment of the sequence with existing databases, which remains limited as these databases might be incomplete and difficult to detect highly divergent viruses. Thus, machine learning and deep learning have been used in this regard, to unveil the patterns that distinguish viral sequences through learning from the NGS data. This study focuses on viral sequence detection using convolutional neural network (CNN). This study intended to investigate how CNN model can be used for analysis of NGS data and develop a CNN model for detecting potential viral sequences from NGS data. The CNN architecture used for this study is based on an existing design that divided into two branches namely pattern and frequency branch that cater for extracting different aspects of information from the data and lastly combined into a full model. This study further implemented slightly modified architecture that includes additional convolution layer and pooling layer. Then, parameter tuning is implemented to identify near optimal parameters for the CNN to elucidate the performance impact. The evaluation of the optimized CNN model is done using a dataset with 18,445 DNA sequences. The results show that the CNN model in this study achieved a better performance compared with existing in terms of area under receiver operating characteristics curve (AUROC) for full model (+0.1434).
{"title":"Detection of Potential Viral Sequence from Next Generation Sequencing Data Using Convolutional Neural Network","authors":"X. Y. Lim, Jia Yee Lim, Weng Howe Chan, Hui Wen Nies","doi":"10.11113/ijic.v13n1.382","DOIUrl":"https://doi.org/10.11113/ijic.v13n1.382","url":null,"abstract":"Next Generation Sequencing (NGS) is a modern sequencing technology that can determine the sequences of RNA and DNA faster and at lower cost. The availability of NGS data has sparked numerous efforts in bioinformatics, especially in the study of genetic variation and viral sequence detection. Viral sequence detection has been one of the important processes in studying virus-induced diseases. Common methods in detecting viral sequences involve alignment of the sequence with existing databases, which remains limited as these databases might be incomplete and difficult to detect highly divergent viruses. Thus, machine learning and deep learning have been used in this regard, to unveil the patterns that distinguish viral sequences through learning from the NGS data. This study focuses on viral sequence detection using convolutional neural network (CNN). This study intended to investigate how CNN model can be used for analysis of NGS data and develop a CNN model for detecting potential viral sequences from NGS data. The CNN architecture used for this study is based on an existing design that divided into two branches namely pattern and frequency branch that cater for extracting different aspects of information from the data and lastly combined into a full model. This study further implemented slightly modified architecture that includes additional convolution layer and pooling layer. Then, parameter tuning is implemented to identify near optimal parameters for the CNN to elucidate the performance impact. The evaluation of the optimized CNN model is done using a dataset with 18,445 DNA sequences. The results show that the CNN model in this study achieved a better performance compared with existing in terms of area under receiver operating characteristics curve (AUROC) for full model (+0.1434).","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"50 1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76994903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rise of interest in cryptocurrency in the recent decade, an ocean of financial news data has surfaced in articles, tweets, and even Reddit posts. Due to the sheer volume, it is not practical for the casual trader to read through all these news sources manually. However, only going through one or two sources alone may result in receiving biased information, or no useful information at all. With the current rise in cryptocurrency, accurately predicting market trends becomes highly beneficial to the user, providing a major opportunity for lower-income households to have a higher chance of profiting and living a substantially more comfortable lifestyle. In this study, a developer's API key was obtained for three news sources to scrape financial news from. Then, the TensorFlow Keras model and Gensim model's doc2vec NLP tool were utilized to process the data scraped online. The data is then saved as a .model and .sav file, and a website was constructed using the Flask framework. The website is now deployed and is available for all users. However, because the data obtained was too small to be utilized well, only a weak linear model that could give us a correlation between price and news sentiment was able to be constructed. The dashboard passed its functional and UAT tests with 100%, and via the usability test with SUS, the dashboard is considered to be easy to use. In all, the website summarizes the main details and sentiment of the coins and will benefit users who are just being introduced to the cryptocurrency space.
{"title":"Elucidating Cryptocurrency with Trading Dashboard","authors":"Masitah Ghazali, Alison Kuan Rong Wong","doi":"10.11113/ijic.v13n1.391","DOIUrl":"https://doi.org/10.11113/ijic.v13n1.391","url":null,"abstract":"With the rise of interest in cryptocurrency in the recent decade, an ocean of financial news data has surfaced in articles, tweets, and even Reddit posts. Due to the sheer volume, it is not practical for the casual trader to read through all these news sources manually. However, only going through one or two sources alone may result in receiving biased information, or no useful information at all. With the current rise in cryptocurrency, accurately predicting market trends becomes highly beneficial to the user, providing a major opportunity for lower-income households to have a higher chance of profiting and living a substantially more comfortable lifestyle. In this study, a developer's API key was obtained for three news sources to scrape financial news from. Then, the TensorFlow Keras model and Gensim model's doc2vec NLP tool were utilized to process the data scraped online. The data is then saved as a .model and .sav file, and a website was constructed using the Flask framework. The website is now deployed and is available for all users. However, because the data obtained was too small to be utilized well, only a weak linear model that could give us a correlation between price and news sentiment was able to be constructed. The dashboard passed its functional and UAT tests with 100%, and via the usability test with SUS, the dashboard is considered to be easy to use. In all, the website summarizes the main details and sentiment of the coins and will benefit users who are just being introduced to the cryptocurrency space.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"103 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80731309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The helpdesk support system is now essential in ensuring the journey of support services runs more systematically. One of the elements that contribute to the non-uniformity of the question data in the Helpdesk Support System is the diversity of services and users. Most questions asked in the system are in various forms and sentence styles but usually offer the same meaning making its hard for automation of the question classification process. This has led to problems such as the tickets being forwarded to the wrong resolver group, causing the ticket transfer process to take longer response. The key findings in the exploration results revealed that tickets with a high number of transfer transactions take longer to complete than tickets compared to no transfer transaction. Thus, this research aims to develop an automated question classification model for the Helpdesk Support System by applying supervised machine learning methods: Naïve Bayes (NB) and Support Vector Machine (SVM). The domain will use a readily available dataset from the IT Unit. The results using these techniques are then evaluated using confusion matrix and classification report evaluation, including precision, recall, and F1-Measure measurement. The outcomes showed that the SVM algorithm and TF-IDF feature extraction outperformed in terms of accuracy score compared to the NB algorithm. It is expected that this study will have a significant impact on the productivity of team technical and system owners in dealing with the increasing number of comments, feedback, and complaints presented by end-users.
{"title":"Question Classification for Helpdesk Support Forum Using Support Vector Machine and Naïve Bayes Algorithm","authors":"Noor Aklima Harun, S. Huspi, Noorminshah A. Iahad","doi":"10.11113/ijic.v13n1.388","DOIUrl":"https://doi.org/10.11113/ijic.v13n1.388","url":null,"abstract":"The helpdesk support system is now essential in ensuring the journey of support services runs more systematically. One of the elements that contribute to the non-uniformity of the question data in the Helpdesk Support System is the diversity of services and users. Most questions asked in the system are in various forms and sentence styles but usually offer the same meaning making its hard for automation of the question classification process. This has led to problems such as the tickets being forwarded to the wrong resolver group, causing the ticket transfer process to take longer response. The key findings in the exploration results revealed that tickets with a high number of transfer transactions take longer to complete than tickets compared to no transfer transaction. Thus, this research aims to develop an automated question classification model for the Helpdesk Support System by applying supervised machine learning methods: Naïve Bayes (NB) and Support Vector Machine (SVM). The domain will use a readily available dataset from the IT Unit. The results using these techniques are then evaluated using confusion matrix and classification report evaluation, including precision, recall, and F1-Measure measurement. The outcomes showed that the SVM algorithm and TF-IDF feature extraction outperformed in terms of accuracy score compared to the NB algorithm. It is expected that this study will have a significant impact on the productivity of team technical and system owners in dealing with the increasing number of comments, feedback, and complaints presented by end-users.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"111 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91362532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Ismail, N. H. Abdul Wahab, Khairunnisa A. Kadir, S. H, N. Sunar, S. H. S. Ariffin, Nor Shahida Hasan, K. Wong
A platform that enables users to schedule appointments and connect with dentists is called the Dental Services System. The bulk of appointment slot orders are placed through more traditional channels, such phone calls, texts, or clinic entrances, prior to obtaining treatment. When staff members are unable to alter their status or take too long to provide information, schedule a time, or complete an assignment, this might be troublesome. Blockchain technology is a distributed ledger system that makes use of mathematics, algorithms, encryption, and financial factors. Blockchain's high-security design makes it safe, and the immutability of the data stored there helps to increase public confidence. Data storage databases that use blockchain technology and include security features that permit the exploitation of exposed user data are the main subject of the study. The blockchain may be integrated into a dental service system because of its excellence. The goal of this implementation of blockchain technology into a Dental Service System is to guarantee complete confidentiality while enabling authorized users to quickly create and get permanent records when paired with an application layer. The goal of this project is to create a tool that allows users to schedule appointments while utilizing a blockchain for safe data storage. In the end, this application will facilitate user appointment scheduling while limiting third parties' access to user data.
{"title":"Dental Service System into Blockchain Environment","authors":"R. Ismail, N. H. Abdul Wahab, Khairunnisa A. Kadir, S. H, N. Sunar, S. H. S. Ariffin, Nor Shahida Hasan, K. Wong","doi":"10.11113/ijic.v13n1.394","DOIUrl":"https://doi.org/10.11113/ijic.v13n1.394","url":null,"abstract":"A platform that enables users to schedule appointments and connect with dentists is called the Dental Services System. The bulk of appointment slot orders are placed through more traditional channels, such phone calls, texts, or clinic entrances, prior to obtaining treatment. When staff members are unable to alter their status or take too long to provide information, schedule a time, or complete an assignment, this might be troublesome. Blockchain technology is a distributed ledger system that makes use of mathematics, algorithms, encryption, and financial factors. Blockchain's high-security design makes it safe, and the immutability of the data stored there helps to increase public confidence. Data storage databases that use blockchain technology and include security features that permit the exploitation of exposed user data are the main subject of the study. The blockchain may be integrated into a dental service system because of its excellence. The goal of this implementation of blockchain technology into a Dental Service System is to guarantee complete confidentiality while enabling authorized users to quickly create and get permanent records when paired with an application layer. The goal of this project is to create a tool that allows users to schedule appointments while utilizing a blockchain for safe data storage. In the end, this application will facilitate user appointment scheduling while limiting third parties' access to user data.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"132 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85754388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Fakrullah Kamarudin Shah, Marina Md-Arshad, Adlina Abdul Samad, Fuad A. Ghaleb
Cloud computing represents a new epoch in computing. From huge enterprises to individual use, cloud computing always provides an answer. Therefore, cloud computing must be readily accessible and scalable, and customers must pay only for the resources they consume rather than for the entire infrastructure. With such conveniences, come with their own threat especially brute force attacks since the resources are available publicly online for the whole world to see. In a brute force attack, the attacker attempts every possible combination of username and password to obtain access to the system. This study aims to examine the performance of the k-Nearest Neighbours (k-NN) and Decision Tree algorithms by contrasting their precision, recall, and F1 score. This research makes use of the CICIDS2017 dataset, which is a labelled dataset produced by the Canada Institute for Cybersecurity. A signature for the brute force attack is utilised with an Intrusion Detection System (IDS) to detect the attack. This strategy, however, is ineffective when a network is being attacked by a novel or unknown attack or signature. At the conclusion of the study, the performance of both algorithms is evaluated by comparing their precision, recall, and f1 score. The results show that Decision Tree performs slightly better than k-NN at classifying FTP and SSH attacks.
{"title":"Comparing FTP and SSH Password Brute Force Attack Detection using k-Nearest Neighbour (k-NN) and Decision Tree in Cloud Computing","authors":"Muhammad Fakrullah Kamarudin Shah, Marina Md-Arshad, Adlina Abdul Samad, Fuad A. Ghaleb","doi":"10.11113/ijic.v13n1.386","DOIUrl":"https://doi.org/10.11113/ijic.v13n1.386","url":null,"abstract":"Cloud computing represents a new epoch in computing. From huge enterprises to individual use, cloud computing always provides an answer. Therefore, cloud computing must be readily accessible and scalable, and customers must pay only for the resources they consume rather than for the entire infrastructure. With such conveniences, come with their own threat especially brute force attacks since the resources are available publicly online for the whole world to see. In a brute force attack, the attacker attempts every possible combination of username and password to obtain access to the system. This study aims to examine the performance of the k-Nearest Neighbours (k-NN) and Decision Tree algorithms by contrasting their precision, recall, and F1 score. This research makes use of the CICIDS2017 dataset, which is a labelled dataset produced by the Canada Institute for Cybersecurity. A signature for the brute force attack is utilised with an Intrusion Detection System (IDS) to detect the attack. This strategy, however, is ineffective when a network is being attacked by a novel or unknown attack or signature. At the conclusion of the study, the performance of both algorithms is evaluated by comparing their precision, recall, and f1 score. The results show that Decision Tree performs slightly better than k-NN at classifying FTP and SSH attacks.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"49 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86265409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Docker is undeniably powerful and revolutionary in how containerized system development is developed today, but it is apparent that the learning curve for it should be addressed, as it typically is complex at times, especially for beginners. One of the fundamental tasks in a Docker workflow is Dockerfile configurations, which at times require ample time to study and observe for attaining the best practices, even the appropriate result. This issue undeniably affects the developer experience. Developer Experience (DX), being a derived field from User Experience (UX) that has been getting traction for the past few years concerns developers’ innate ability to perceive tasks as enjoyable, painful, or perhaps some other sets of emotions. The goal of DX is to evaluate all those factors in order to improve the software development experience, which consequently affects how the project is delivered. In resonance with that, this work aims to enhance the DX by way of proposing and incorporating supporting interaction tools, both based on CLI and GUI as the interface type, with two different permutations: CLI and GUI. The DX of both has to be evaluated by the experts, who are of experienced developers, regardless of whether they have knowledge of Docker or not. The method to test and evaluate two different solutions is conducted qualitatively, with each respondent having a different order of evaluating the two solutions. The qualitative data is thematically analyzed, resulting in GUI being the best option among the two. The contribution of this research is the design guidelines for GUI and CLI-based tools development that enhance the Developer Experience (DX) in the scaffolding of Dockerfile and docker-compose.yml for projects that use Docker.
{"title":"Enhancing the Developer Experience (DX) in Docker Supported Projects","authors":"Masitah Ghazali, Alfian Naufal Ravi Hidayat","doi":"10.11113/ijic.v13n1.393","DOIUrl":"https://doi.org/10.11113/ijic.v13n1.393","url":null,"abstract":"Docker is undeniably powerful and revolutionary in how containerized system development is developed today, but it is apparent that the learning curve for it should be addressed, as it typically is complex at times, especially for beginners. One of the fundamental tasks in a Docker workflow is Dockerfile configurations, which at times require ample time to study and observe for attaining the best practices, even the appropriate result. This issue undeniably affects the developer experience. Developer Experience (DX), being a derived field from User Experience (UX) that has been getting traction for the past few years concerns developers’ innate ability to perceive tasks as enjoyable, painful, or perhaps some other sets of emotions. The goal of DX is to evaluate all those factors in order to improve the software development experience, which consequently affects how the project is delivered. In resonance with that, this work aims to enhance the DX by way of proposing and incorporating supporting interaction tools, both based on CLI and GUI as the interface type, with two different permutations: CLI and GUI. The DX of both has to be evaluated by the experts, who are of experienced developers, regardless of whether they have knowledge of Docker or not. The method to test and evaluate two different solutions is conducted qualitatively, with each respondent having a different order of evaluating the two solutions. The qualitative data is thematically analyzed, resulting in GUI being the best option among the two. The contribution of this research is the design guidelines for GUI and CLI-based tools development that enhance the Developer Experience (DX) in the scaffolding of Dockerfile and docker-compose.yml for projects that use Docker.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"47 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76875545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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